The proposed implementation uses an \(\mathsf {AE}\)  learning system based on a convolutional neural network (CNN).
The general structure of an \(\mathsf {AE}\)  consists of two main blocks: the encoder \(f(x̑)\)  and the decoder \(g(x̑)\) , where \(x̑\)  is the input data. The \(\mathsf {AE}\)  is trained to minimize a certain joint loss function, which we denote by \(\mathcal {L}(x̑,g(f(x̑)))\) .
An end-to-end communication system can be interpreted as an \(\mathsf {AE}\)  in which the encoder and the decoder are part of the transmitter and the receiver respectively, and they can be jointly optimized through an end-to-end learning procedure.
AEs have been applied in recent years to various wireless communication tasks, such as MIMO detection [1]}, channel coding [2]} and blind channel equalization [3]}.
